求信息熵matlab程序！！！

wy558558558

哪位大侠有关于信息熵的matlab 程序，让俺参考一下呗！！急，急！！！！包括奇异谱商、功率谱熵、小波空间特征谱商和小波能谱商。请各位大侠把相关的程序发到我的邮箱：feic544@163.com.谢谢啦！！

friendchj

根据公式自己先编程试试

gouchunfang

你好！我这有本书你参考一下！

xiaosi204

给我也发一份谢谢！ye_zi204@163.com

tiger861128

能不能把那本书也发给我呀，救急啊...谢谢啦

hcdhuang2008

我也需要，定重谢！！！！邮箱hcdhuang2008@163.com

duqi

能不能把那本书的名称和作者留一下。。。。

kingsir

书的名字能说吗？

binghankun

楼主  麻烦问下  你这程序搞定了吗？

malan018

也给我发一下吧，谢谢！

小周学软件

同求啊，我想问hilbert谱熵的求解

happy

可以参考下面的函数

1. function [estimate,nbias,sigma,descriptor]=entropy(x,descriptor,approach,base)
   
2. %ENTROPY   Estimates the entropy of stationary signals with
   
3. %          independent samples using various approaches.
   
4. %   [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = ENTROPY(X) or
   
5. %   [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = ENTROPY(X,DESCRIPTOR) or
   
6. %   [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = ENTROPY(X,DESCRIPTOR,APPROACH) or
   
7. %   [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = ENTROPY(X,DESCRIPTOR,APPROACH,BASE)
   
8. %
   
9. %   ESTIMATE    : The entropy estimate
   
10. %   NBIAS       : The N-bias of the estimate
    
11. %   SIGMA       : The standard error of the estimate
    
12. %   DESCRIPTOR  : The descriptor of the histogram, seel alse ENTROPY
    
13. %
    
14. %   X           : The time series to be analyzed, a row vector
    
15. %   DESCRIPTOR  : Where DESCRIPTOR=[LOWERBOUND,UPPERBOUND,NCELL]
    
16. %     LOWERBOUND: Lowerbound of the histogram
    
17. %     UPPERBOUND: Upperbound of the histogram
    
18. %     NCELL     : The number of cells of the histogram      
    
19. %   APPROACH    : The method used, one of the following ones:
    
20. %     'unbiased': The unbiased estimate (default)
    
21. %     'mmse'    : The minimum mean square error estimate
    
22. %     'biased'  : The biased estimate
    
23. %   BASE        : The base of the logarithm; default e
    
24. %
    
25. %   See also: http://www.cs.rug.nl/~rudy/matlab/
    
26. 
    
27. %   R. Moddemeijer
    
28. %   Copyright (c) by R. Moddemeijer
    
29. %   $Revision: 1.1 $  $Date: 2001/02/05 08:59:36 $
    
30. 
    
31. 
    
32. 
    
33. if nargin <1
    
34.    disp('Usage: [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = ENTROPY(X)')
    
35.    disp('       [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = ENTROPY(X,DESCRIPTOR)')
    
36.    disp('       [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = ENTROPY(X,DESCRIPTOR,APPROACH)')
    
37.    disp('       [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = ENTROPY(X,DESCRIPTOR,APPROACH,BASE)')
    
38.    disp('Where: DESCRIPTOR = [LOWERBOUND,UPPERBOUND,NCELL]')
    
39.    return
    
40. end
    
41. 
    
42. % Some initial tests on the input arguments
    
43. 
    
44. [NRowX,NColX]=size(x);
    
45. 
    
46. if NRowX~=1
    
47.   error('Invalid dimension of X');
    
48. end;
    
49. 
    
50. if nargin>4
    
51.   error('Too many arguments');
    
52. end;
    
53. 
    
54. if nargin==1
    
55.   [h,descriptor]=histogram(x);
    
56. end;
    
57. 
    
58. if nargin>=2
    
59.   [h,descriptor]=histogram(x,descriptor);
    
60. end;
    
61. 
    
62. if nargin<3
    
63.   approach='unbiased';
    
64. end;
    
65. 
    
66. if nargin<4
    
67.   base=exp(1);
    
68. end;
    
69. 
    
70. lowerbound=descriptor(1);
    
71. upperbound=descriptor(2);
    
72. ncell=descriptor(3);
    
73. 
    
74. estimate=0;
    
75. sigma=0;
    
76. count=0;
    
77. 
    
78. for n=1:ncell
    
79.   if h(n)~=0
    
80.     logf=log(h(n));
    
81.   else
    
82.     logf=0;
    
83.   end;
    
84.   count=count+h(n);
    
85.   estimate=estimate-h(n)*logf;
    
86.   sigma=sigma+h(n)*logf^2;
    
87. end;
    
88. 
    
89. % biased estimate
    
90. 
    
91. estimate=estimate/count;
    
92. sigma   =sqrt( (sigma/count-estimate^2)/(count-1) );
    
93. estimate=estimate+log(count)+log((upperbound-lowerbound)/ncell);
    
94. nbias   =-(ncell-1)/(2*count);
    
95. 
    
96. % conversion to unbiased estimate
    
97. 
    
98. if approach(1)=='u'
    
99.   estimate=estimate-nbias;
    
100.   nbias=0;
     
101. end;
     
102. 
     
103. % conversion to minimum mse estimate
     
104. 
     
105. if approach(1)=='m'
     
106.   estimate=estimate-nbias;
     
107.   nbias=0;
     
108.   lambda=estimate^2/(estimate^2+sigma^2);
     
109.   nbias   =(1-lambda)*estimate;
     
110.   estimate=lambda*estimate;
     
111.   sigma   =lambda*sigma;
     
112. end;
     
113. 
     
114. % base transformation
     
115. 
     
116. estimate=estimate/log(base);
     
117. nbias   =nbias   /log(base);
     
118. sigma   =sigma   /log(base);
     

复制代码

我是了了

12楼的很有参考价值啊！谢谢，打算用hht后求能量熵分辨故障点，谢谢！

箐树湖都

楼主能不能也给我发一下，万分感谢！sylvia1987@qq.com

ipod

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